The application of artificial intelligence in spine surgery

Optimizing the clinical adoption of total joint replacement of the lumbar spine through imaging, robotics and artificial intelligence

INTRODUCTION

The objective of this article is to assess the potential of imaging, robotics, and artificial intelligence (AI) to significantly improve spine care, preoperative planning and surgery.

AREAS COVERED

This article describes the development of lumbar total joint replacement (TJR) of the spine (MOTUS, 3Spine, Chattanooga, TN, U.S.A.). We discuss the evolution of intra-operative imaging, robotics, and AI and how these trends can intersect with lumbar TJR to optimize the safety, efficiency, and accessibility of the procedure.

EXPERT OPINION

By preserving natural spinal motion, TJR represents a significant leap forward in the treatment of degenerative spinal conditions by providing an alternative to fusion. This transformation has already occurred and is continuing to evolve in the primary synovial joints such as hip, knee, shoulder and ankle where arthroplasty outcomes are now so superior that fusion is considered a salvage procedure. The convergence of imaging, robotics and AI is poised to reshape spine care by enhancing precision and safety, personalizing treatment pathways, lowering production costs, and accelerating adoption. However, the key challenges include ensuring continued collaboration between surgeons, researchers, manufacturers, and regulatory bodies to optimize the potential of TJR.

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain

Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack of objective pain biomarkers. The present study applies machine learning to predict which patients will respond to SCS based on intraoperative electroencephalogram (EEG) data and recognized outcome measures. The study included 20 chronic pain patients who were undergoing SCS surgery. During intraoperative monitoring, EEG signals were recorded under SCS OFF (baseline) and ON conditions, including tonic and high density (HD) stimulation. Once spectral EEG features were extracted during offline analysis, principal component analysis (PCA) and a recursive feature elimination approach were used for feature selection. A subset of EEG features, clinical characteristics of the patients and preoperative patient reported outcome measures (PROMs) were used to build a predictive model. Responders and nonresponders were grouped based on 50% reduction in 3-month postoperative Numeric Rating Scale (NRS) scores. The two groups had no statistically significant differences with respect to demographics (including age, diagnosis, and pain location) or PROMs, except for the postoperative NRS (worst pain: p = 0.028; average pain: p < 0.001) and Oswestry Disability Index scores (ODI, p = 0.030). Alpha-theta peak power ratio differed significantly between CP3-CP4 and T3-T4 (p = 0.019), with the lowest activity in CP3-CP4 during tonic stimulation. The decision tree model performed best, achieving 88.2% accuracy, an F1 score of 0.857, and an area under the curve (AUC) of the receiver operating characteristic (ROC)  of 0.879. Our findings suggest that combination of subjective self-reports, intraoperatively obtained EEGs, and well-designed machine learning algorithms might be potentially used to distinguish responders and nonresponders. Machine and deep learning hold enormous potential to predict patient responses to SCS therapy resulting in refined patient selection and improved patient outcomes.

Framework for Adoption of Enabling Technologies for Improved Outcomes in Spine Surgery

STUDY DESIGN

Narrative Review.

OBJECTIVES

We aim to investigate the integration and impact of enabling technologies, such as augmented reality, virtual reality, mixed reality, navigation, robotics, and artificial intelligence within the domain of spinal surgery.

METHODS

We made a literature review for articles that examined the progression of adoption from initial to subsequent adopters. We also analysed the key determinants that influence adopting these technologies into clinical settings. These include cost-effectiveness, ease of integration, patient acceptance, learning curves, and availability of training resources. Based on the available data a suggestion has been made on the adoption framework for clinical utility.

RESULTS

These technological advancements have the potential to transform surgical practice, offering improved precision and efficiency. The journey toward widespread adoption presents challenges, which include the financial implications, the necessity for specialized training, and the complexities associated with integration. To navigate these hurdles, the study proposes recommendations aimed at improving cost efficiency, streamlining technology integration, investing in professional development, and nurturing a culture of innovation and research.

CONCLUSIONS

A framework has been established for the evaluation and integration of state-of-the-art technologies in spinal surgery, thereby maximizing their potential impact on surgical outcomes and patient welfare.

The multiple uses of artificial intelligence in exercise programs: a narrative review

Background

Artificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. Machine learning, a subset of artificial intelligence, concentrates on empowering computers to learn and enhance from data autonomously; this narrative review seeks to elucidate the utilization of artificial intelligence in fostering physical activity, training, exercise, and health outcomes, addressing a significant gap in the comprehension of practical applications.

Methods

Only Randomized Controlled Trials (RCTs) published in English were included. Inclusion criteria: all RCTs that use artificial intelligence to program, supervise, manage, or assist physical activity, training, exercise, or health programs. Only studies published from January 1, 2014, were considered. Exclusion criteria: all the studies that used robot-assisted, robot-supported, or robotic training were excluded.

Results

A total of 1772 studies were identified. After the first stage, where the duplicates were removed, 1,004 articles were screened by title and abstract. A total of 24 studies were identified, and finally, after a full-text review, 15 studies were identified as meeting all eligibility criteria for inclusion. The findings suggest that artificial intelligence holds promise in promoting physical activity across diverse populations, including children, adolescents, adults, older adult, and individuals with disabilities.

Conclusion

Our research found that artificial intelligence, machine learning and deep learning techniques were used: (a) as part of applications to generate automatic messages and be able to communicate with users; (b) as a predictive approach and for gesture and posture recognition; (c) as a control system; (d) as data collector; and (e) as a guided trainer.

Artificial intelligence and its use in spine surgery and preparation of predictive models: a systematic review

During the last decade, artificial intelligence (AI) has witnessed phenomenal growth, accompanied by unparalleled opportunities in health. The integration of machine learning at the heart of health systems has shaped a revolution that helps to reduce health, social, and economic inequities while improving global health outcomes. The use of AI for spine surgery, in particular, serves to enhance the accuracy of predicted algorithms and can be used to improve surgical accuracy and reduce operative time, thereby enhancing efficiency and productivity. This review outlines the current literature on the use of AI in spine surgery and identifies its established evidence to provide positive surgical outcomes. With all these promises, AI in spine surgery remains in its infancy; it is anticipated that further development will yield much greater benefits in the immediate future.

Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature

PURPOSE

To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance.

METHODS

A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland-Altman plot were used to evaluate the performance of the cascade HRNet model.

RESULTS

The PCK of the cascaded HRNet model was 97.9-100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989-0.999, R = 0.991-0.999, MAE = 0.63-1.65, MSE = 0.61-4.06, RMSE = 0.78-2.01).

CONCLUSION

The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.

The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks

We aimed to develop and validate a deep convolutional neural network (DCNN) model capable of accurately identifying spondylolysis or spondylolisthesis on lateral or dynamic X-ray images. A total of 2449 lumbar lateral and dynamic X-ray images were collected from two tertiary hospitals. These images were categorized into lumbar spondylolysis (LS), degenerative lumbar spondylolisthesis (DLS), and normal lumbar in a proportional manner. Subsequently, the images were randomly divided into training, validation, and test sets to establish a classification recognition network. The model training and validation process utilized the EfficientNetV2-M network. The model's ability to generalize was assessed by conducting a rigorous evaluation on an entirely independent test set and comparing its performance with the diagnoses made by three orthopedists and three radiologists. The evaluation metrics employed to assess the model's performance included accuracy, sensitivity, specificity, and F1 score. Additionally, the weight distribution of the network was visualized using gradient-weighted class activation mapping (Grad-CAM). For the doctor group, accuracy ranged from 87.9 to 90.0% (mean, 89.0%), precision ranged from 87.2 to 90.5% (mean, 89.0%), sensitivity ranged from 87.1 to 91.0% (mean, 89.2%), specificity ranged from 93.7 to 94.7% (mean, 94.3%), and F1 score ranged from 88.2 to 89.9% (mean, 89.1%). The DCNN model had accuracy of 92.0%, precision of 91.9%, sensitivity of 92.2%, specificity of 95.7%, and F1 score of 92.0%. Grad-CAM exhibited concentrations of highlighted areas in the intervertebral foraminal region. We developed a DCNN model that intelligently distinguished spondylolysis or spondylolisthesis on lumbar lateral or lumbar dynamic radiographs.

Advancing healthcare: the role and impact of AI and foundation models

BACKGROUND

The integration of artificial intelligence (AI) into the healthcare domain is a monumental shift with profound implications for diagnostics, medical interventions, and the overall structure of healthcare systems.

PURPOSE

This study explores the transformative journey of foundation AI models in healthcare, shedding light on the challenges, ethical considerations, and vast potential they hold for improving patient outcome and system efficiency. Notably, in this investigation we observe a relatively slow adoption of AI within the public sector of healthcare. The evolution of AI in healthcare is un-paralleled, especially its prowess in revolutionizing diagnostic processes.

RESULTS

This research showcases how these foundational models can unravel hidden patterns within complex medical datasets. The impact of AI reverberates through medical interventions, encompassing pathology, imaging, genomics, and personalized healthcare, positioning AI as a cornerstone in the quest for precision medicine. The paper delves into the applications of generative AI models in critical facets of healthcare, including decision support, medical imaging, and the prediction of protein structures. The study meticulously evaluates various AI models, such as transfer learning, RNN, autoencoders, and their roles in the healthcare landscape. A pioneering concept introduced in this exploration is that of General Medical AI (GMAI), advocating for the development of reusable and flexible AI models.

CONCLUSION

The review article discusses how AI can revolutionize healthcare by stressing the significance of transparency, fairness and accountability, in AI applications regarding patient data privacy and biases. By tackling these issues and suggesting a governance structure the article adds to the conversation about AI integration in healthcare environments.

Intra-Examiner Reliability and Validity of Sagittal Cervical Spine Mensuration Methods Using Deep Convolutional Neural Networks

Background: The biomechanical analysis of spine and postural misalignments is important for surgical and non-surgical treatment of spinal pain. We investigated the examiner reliability of sagittal cervical alignment variables compared to the reliability and concurrent validity of computer vision algorithms used in the PostureRay® software 2024. Methods: A retrospective database of 254 lateral cervical radiographs of patients between the ages of 11 and 86 is studied. The radiographs include clearly visualized C1-C7 vertebrae that were evaluated by a human using the software. To evaluate examiner reliability and the concurrent validity of the trained CNN performance, two blinded trials of radiographic digitization were performed by an extensively trained expert user (US) clinician with a two-week interval between trials. Then, the same clinician used the trained CNN twice to reproduce the same measures within a 2-week interval on the same 254 radiographs. Measured variables included segmental angles as relative rotation angles (RRA) C1-C7, Cobb angles C2-C7, relative segmental translations (RT) C1-C7, anterior translation C2-C7, and absolute rotation angle (ARA) C2-C7. Data were remotely extracted from the examiner's PostureRay® system for data collection and sorted based on gender and stratification of degenerative changes. Reliability was assessed via intra-class correlations (ICC), root mean squared error (RMSE), and R2 values. Results: In comparing repeated measures of the CNN network to itself, perfect reliability was found for the ICC (1.0), RMSE (0), and R2 (1). The reliability of the trained expert US was in the excellent range for all variables, where 12/18 variables had ICCs ≥ 0.9 and 6/18 variables were 0.84 ≤ ICCs ≤ 0.89. Similarly, for the expert US, all R2 values were in the excellent range (R2 ≥ 0.7), and all RMSEs were small, being 0.42 ≤ RMSEs ≤ 3.27. Construct validity between the expert US and the CNN network was found to be in the excellent range with 18/18 ICCs in the excellent range (ICCs ≥ 0.8), 16/18 R2 values in the strong to excellent range (R2 ≥ 0.7), and 2/18 in the good to moderate range (R2 RT C6/C7 = 0.57 and R2 Cobb C6/C7 = 0.64. The RMSEs for expert US vs. the CNN network were small, being 0.37 ≤ RMSEs ≤ 2.89. Conclusions: A comparison of repeated measures within the computer vision CNN network and expert human found exceptional reliability and excellent construct validity when comparing the computer vision to the human observer.

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.

One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning

OBJECTIVE

Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS), although identification of the median nerve (MN) and diagnosis of CTS depend heavily on the expertise of examiners. In the aim of alleviating this problem, we developed a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluated its effectiveness as a computer-aided diagnostic tool.

METHODS

We combined real-time MN delineation, accurate biometric measurements and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We then collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol.

RESULTS

The proposed model exhibited better segmentation and measurement performance than competing methods, with a Hausdorff distance (95th percentile) score of 7.21 px, average symmetric surface distance score of 2.64 px, Dice score of 85.78% and intersection over union score of 76.00%. In the reader study, it exhibited performance comparable to the average performance of experienced radiologists in classifying CTS and outperformed inexperienced radiologists in terms of classification metrics (e.g., accuracy score 3.59% higher and F1 score 5.85% higher).

CONCLUSION

Diagnostic performance of the OSA-CTSD was promising, with the advantages of real-time delineation, automation and clinical interpretability. The application of such a tool not only reduces reliance on the expertise of examiners but also can help to promote future standardization of the CTS diagnostic process, benefiting both patients and radiologists.

Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review

This systematic review summarizes existing evidence and outlines the benefits of artificial intelligence-assisted spine surgery. The popularity of artificial intelligence has grown significantly, demonstrating its benefits in computer-assisted surgery and advancements in spinal treatment. This study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a set of reporting guidelines specifically designed for systematic reviews and meta-analyses. The search strategy used Medical Subject Headings (MeSH) terms, including "MeSH (Artificial intelligence)," "Spine" AND "Spinal" filters, in the last 10 years, and English- from January 1, 2013, to October 31, 2023. In total, 442 articles fulfilled the first screening criteria. A detailed analysis of those articles identified 220 that matched the criteria, of which 11 were considered appropriate for this analysis after applying the complete inclusion and exclusion criteria. In total, 11 studies met the eligibility criteria. Analysis of these studies revealed the types of artificial intelligence-assisted spine surgery. No evidence suggests the superiority of assisted spine surgery with or without artificial intelligence in terms of outcomes. In terms of feasibility, accuracy, safety, and facilitating lower patient radiation exposure compared with standard fluoroscopic guidance, artificial intelligence-assisted spine surgery produced satisfactory and superior outcomes. The incorporation of artificial intelligence with augmented and virtual reality appears promising, with the potential to enhance surgeon proficiency and overall surgical safety.

Comparison between model-based RSA and an AI-based CT-RSA: an accuracy study of 30 patients

BACKGROUND AND PURPOSE

Radiostereometry (RSA) is the current gold standard for evaluating early implant migration. CT-based migration analysis is a promising method, with fewer handling requirements compared with RSA and no need for implanted bone-markers. We aimed to evaluate agreement between a new artificial intelligence (AI)-based CT-RSA and model-based RSA (MBRSA) in measuring migration of cup and stem in total hip arthroplasty (THA).

PATIENTS AND METHODS

30 patients with THA for primary osteoarthritis (OA) were included. RSA examinations were performed on the first postoperative day, and at 2 weeks, 3 months, 1, 2, and 5 years after surgery. A low-dose CT scan was done at 2 weeks and 5 years. The agreement between the migration results obtained from MBRSA and AI-based CT-RSA was assessed using Bland-Altman plots.

RESULTS

Stem migration (y-translation) between 2 weeks and 5 years, for the primary outcome measure, was -0.18 (95% confidence interval [CI] -0.31 to -0.05) mm with MBRSA and -0.36 (CI -0.53 to -0.19) mm with AI-based CT-RSA. Corresponding proximal migration of the cup (y-translation) was 0.06 (CI 0.02-0.09) mm and 0.02 (CI -0.01 to 0.05) mm, respectively. The mean difference for all stem and cup comparisons was within the range of MBRSA precision. The AI-based CT-RSA showed no intra- or interobserver variability.

CONCLUSION

We found good agreement between the AI-based CT-RSA and MBRSA in measuring postoperative implant migration. AI-based CT-RSA ensures user independence and delivers consistent results.

Machine Learning in Spine Surgery: A Narrative Review

Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

Emerging Technologies within Spine Surgery

New innovations within spine surgery continue to propel the field forward. These technologies improve surgeons' understanding of their patients and allow them to optimize treatment planning both in the operating room and clinic. Additionally, changes in the implants and surgeon practice habits continue to evolve secondary to emerging biomaterials and device design. With ongoing advancements, patients can expect enhanced preoperative decision-making, improved patient outcomes, and better intraoperative execution. Additionally, these changes may decrease many of the most common complications following spine surgery in order to reduce morbidity, mortality, and the need for reoperation. This article reviews some of these technological advancements and how they are projected to impact the field. As the field continues to advance, it is vital that practitioners remain knowledgeable of these changes in order to provide the most effective treatment possible.

Development of a Machine Learning-Based Model for Accurate Detection and Classification of Cervical Spine Fractures Using CT Imaging

Cervical spine fractures represent a significant healthcare challenge, necessitating accurate detection for appropriate management and improved patient outcomes. This study aims to develop a machine learning-based model utilizing a computed tomography (CT) image dataset to detect and classify cervical spine fractures. Leveraging a large dataset of 4,050 CT images obtained from the Radiological Society of North America (RSNA) Cervical Spine Fracture dataset, we evaluate the potential of machine learning and deep learning algorithms in achieving accurate and reliable cervical spine fracture detection. The model demonstrates outstanding performance, achieving an average precision of 1 and 100% precision, recall, sensitivity, specificity, and accuracy values. These exceptional results highlight the potential of machine learning algorithms to enhance clinical decision-making and facilitate prompt treatment initiation for cervical spine fractures. However, further research and validation efforts are warranted to assess the model's generalizability across diverse populations and real-world clinical settings, ultimately contributing to improved patient outcomes in cervical spine fracture cases.

Deep learning system assisted detection and localization of lumbar spondylolisthesis

Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors' evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis. Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals' evaluation. Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s. Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.

Artificial Intelligence in Surgical Learning

(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.